UNICEF REPORT

When Hope Fades: The Story of Najir

Author: Anthea Petrillo
Published: April 27, 2025

Report Objective

This report aims to raise awareness about the tragic reality of child deaths caused by diarrhoea in children under five. Although preventable and treatable condition, it remains one of the leading causes of child mortality in many low-income countries, where access to healthcare is limited.

The narrative voice of this report is that of Najir, a 4-year-old child who tells his story: from when he tells his mother he has a stomach ache, to when, despite efforts to find a cure, he sadly does not survive. Although this is a fictional story, it is plausible enough to reflect what happens in countries with low access to diarrhoea care, where the inadequacy of healthcare resources is tied to economic and social inequalities.

Data Sources

The data used in this report comes from the following datasets:

  1. unicef_indicator_1.csv: provides global data on the percentage of children under five with diarrhoea who can access healthcare facilities
  2. unicef_indicator_2.csv: offers global data on the percentage of healthcare facilities with basic water access
  3. unicef_metadata.csv: includes economic and social data
  4. deaths_caused_by_diarrhoea.csv: provides data on diarrhoea-related child mortality categorized by seven regions. You can download it here.

Please find the Tableau version of this report here.

Najir’s Story

This is me

My name is Najir, and I am four years old.

I do not feel well. A sharp pain grips my stomach, and my mother tells me I have diarrhoea. She holds my hand tightly, her face etched with worry, her eyes filled with silent fear. “We must find help,” she says. However, seeking medical care is not a simple task.

I live in one of the nineteen countries where, according to data collected between 2017 and 2023, among all the children under the age of five suffering from diarrhoea, fewer than 40% have access to medical advice or treatment at a healthcare facility or from a trained provider.

Code 
import pandas as pd
from plotnine import ggplot, aes, geom_bar, labs, theme_minimal, theme, element_text, scale_fill_gradient

df = pd.read_excel("unicef_indicator_1_filtered_firstgraph.xlsx")

bar_chart = (
    ggplot(df, aes(x='country', y='access to care for diarrhoea', fill='access to care for diarrhoea')) 
    + geom_bar(stat='identity', show_legend=False, width=0.2)  
    + scale_fill_gradient(low='#D5006D', high='#FF80AB') 
    + labs(
        title='Healthcare access for children with diarrhoea in lowest-access countries', 
        x='Country', 
        y='Access to Care for Diarrhoea (%)'
    ) 
    + theme_minimal() 
    + theme(
        axis_text_x=element_text(rotation=45, ha='right'),  
        figure_size=(10, 5), 
        plot_title=element_text(size=14, weight='bold'), 
        axis_title=element_text(size=12)        
    )
)

# Show the chart
bar_chart

A serious public health crisis is evident: the limited accessibility and availability of essential healthcare services in specific areas of the world.

Despite being a preventable and treatable condition, diarrhoea remains one of the leading causes of child mortality in countries where healthcare systems are fragile. According to the 2021 data, diarrhoea accounted for 9% of all deaths in children under 5 — over 1,200 daily, or 440,000 annually.

Mortality is highest in Sub-Saharan Africa.

Code 
import pandas as pd
import plotly.express as px

# Carica il dataset
df = pd.read_csv("unicef_indicator_2 (2).csv")

# Filtra i dati per l'anno 2022 e unità di misura in %
df_2021 = df[
    (df["time_period"] == 2021) &
    (df["unit_of_measure"] == "%")
]

# Crea una colonna formattata per l'hover
df_2021["% Water Access"] = df_2021["obs_value"].apply(lambda x: f"{x:.1f}%")

# Crea la mappa
fig = px.choropleth(
    df_2021,
    locations="alpha_3_code",
    color="obs_value",
    hover_name="country",
    hover_data={
        "alpha_3_code": False,
        "obs_value": False,
        "% Water Access": True
    },
    color_continuous_scale="Blues",
    title="<b>Health care facilities & water access: country comparison 2021</b>"
)

fig.update_layout(
    coloraxis_colorbar=dict(
        title="Water Access (%)",
        tickformat=".1f"
    )
)

fig.show()
C:\Users\user\AppData\Local\Temp\ipykernel_6004\812227575.py:14: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

Fortunately, my mother managed to find a healthcare facility and took me there.
However, the healthcare facilities with access to basic water services is not the same across the world.
The lack of access to basic water services means that healthcare facilities are unable to treat children suffering from diarrhoea.

Code 
import pandas as pd
from plotnine import ggplot, aes, geom_point, geom_smooth, labs, theme_minimal, theme, element_text

# Carica i dati
df = pd.read_csv("unicef_metadata_7.csv")

# Filtra per l'anno 2021
df_2021 = df[df["year"] == 2021]

# Rimuove righe con valori mancanti
df_2021 = df_2021.dropna(subset=["Birth rate, crude (per 1,000 people)", "Life expectancy at birth, total (years)"])

# Crea il grafico
grafico = (
    ggplot(df_2021, aes(
        x="Birth rate, crude (per 1,000 people)",
        y="Life expectancy at birth, total (years)"
    )) 
    + geom_point(color="#FF80AB", size=2.5)  # viola medio
    + geom_smooth(method='lm', color="#4B0082", se=False)  # viola scurissimo
    + labs(
        title='Demographic patterns: Birth rate & Life expectancy (2021)',
        x="Birth Rate (per 1,000 people)",
        y="Life Expectancy (years)"
    )
    + theme_minimal()
    + theme(
        plot_title=element_text(size=14, weight='bold', ha='center'),
        axis_title=element_text(size=12)
    )
)

grafico

Code 
import pandas as pd
from plotnine import ggplot, aes, geom_line, labs, theme_minimal, theme, element_text, scale_color_manual

# Carica i dati
df = pd.read_csv("unicef_metadata_7.csv")

# Definisci i gruppi di paesi
high_birth = ["Niger", "Chad", "Somalia", "Central African Republic", "Mali",
              "Congo", "Nigeria", "Uganda", "Mozambique", "Benin"]
low_birth = ["South Korea", "Hong Kong", "Puerto Rico", "Japan", "Andorra", 
             "San Marino", "Italy", "China", "Spain", "Greece"]

# Assegna etichette personalizzate
df["group"] = df["country"].apply(
    lambda x: "Top 10 High Birth Rate Countries" if x in high_birth else (
              "Top 10 Low Birth Rate Countries" if x in low_birth else None)
)

# Filtra solo i gruppi rilevanti
df_grouped = df[df["group"].notna()]

# Elimina righe con valori nulli
df_grouped = df_grouped.dropna(subset=["Birth rate, crude (per 1,000 people)"])

# Calcola la media annuale per ogni gruppo
media_anni = (
    df_grouped
    .groupby(["year", "group"])["Birth rate, crude (per 1,000 people)"]
    .mean()
    .reset_index()
)

# Crea il grafico
grafico = (
    ggplot(media_anni, aes(x="year", y="Birth rate, crude (per 1,000 people)", color="group")) +
    geom_line(size=1.5) +
    scale_color_manual(values={
        "Top 10 High Birth Rate Countries": "#FF69B4",  # rosa
        "Top 10 Low Birth Rate Countries": "#800080"   # viola
    }) +
    labs(
        title="Birth Rate Trends: High vs Low Rate Countries",
        x="Year",
        y="Average Birth Rate (per 1,000 people)",
        color="Country Group"
    ) +
    theme_minimal() +
    theme(
        plot_title=element_text(size=14, weight='bold', color="#4B0082"),  # viola più scuro
        axis_title=element_text(size=12),
        legend_title=element_text(size=11),
        legend_text=element_text(size=10),
        figure_size=(8, 6)  # grafico più stretto
    )
)

grafico

Code 
import pandas as pd
import plotly.express as px

# Carica il dataset
df = pd.read_csv("unicef_metadata_7.csv")

# Filtra i dati per l'anno 2022 e pulisci
df_2022 = df[df["year"] == 2022]
df_cleaned = df_2022.dropna(subset=["GDP per capita (constant 2015 US$)", "Birth rate, crude (per 1,000 people)"])
df_cleaned["Birth rate, crude (per 1,000 people)"] = df_cleaned["Birth rate, crude (per 1,000 people)"].round(1)

# Crea il grafico con le personalizzazioni richieste
fig = px.scatter(
    df_cleaned,
    x="GDP per capita (constant 2015 US$)",
    y="Birth rate, crude (per 1,000 people)",
    title="<b>GDP per capita and its effect on birth rate</b>",  # Titolo in grassetto
    labels={
        "GDP per capita (constant 2015 US$)": "GDP per capita (USD)",
        "Birth rate, crude (per 1,000 people)": "Birth rate (per 1,000 people)"
    },
    color_discrete_sequence=["#FDB79A"],  # Colore rosa per i punti
    trendline="lowess",
    trendline_color_override="#FF69B4"  # Linea viola
)

# Personalizzazione avanzata
fig.update_traces(
    mode='markers',
    marker=dict(
        size=8,
        opacity=0.7,
        line=dict(width=1, color='DarkSlateGrey')
    ),
    selector=dict(mode='markers')
)

# Migliora l'aspetto della linea di tendenza
fig.update_traces(
    line=dict(width=4),
    selector=dict(type='scatter', mode='lines')
)

# Aggiorna il layout con le nuove specifiche
fig.update_layout(
    title={
        'text': "<b>GDP per capita and its effect on birth rate</b>",
        'y':0.9,
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top',
        'font': dict(
            size=18,
            color='#800080'  # Titolo viola
        )
    },
    xaxis_title="GDP per capita (USD)",
    yaxis_title="Birth rate (per 1,000 people)",
    template="plotly_white",
    font=dict(
        family="Arial",
        size=12,
        color="black"  # Testo assi in nero
    ),
    hovermode="closest",
    showlegend=False,
    xaxis=dict(
        range=[0, 100000],
        tickvals=[0, 20000, 40000, 60000, 80000, 100000],
        tickformat=",",
        title_font=dict(color="black"),  # Asse X in nero
        tickfont=dict(color="black")
    ),
    yaxis=dict(
        range=[0, 45],
        tickvals=[0, 5, 10, 15, 20, 25, 30, 35, 40, 45],
        title_font=dict(color="black"),  # Asse Y in nero
        tickfont=dict(color="black")
    )
)

# Aggiungi annotazione fonte dati
fig.add_annotation(
    x=0.5,
    y=-0.15,
    xref="paper",
    yref="paper",
    text="Fonte: Dati UNICEF 2022",
    showarrow=False,
    font=dict(
        size=10,
        color="grey"
    )
)

fig
C:\Users\user\AppData\Local\Temp\ipykernel_6004\2718777199.py:10: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
Code 
import pandas as pd

# Carica il file
df = pd.read_csv('unicef_metadata_7.csv')

# Assicurati che il GDP per capita sia numerico
df['GDP per capita (constant 2015 US$)'] = pd.to_numeric(df['GDP per capita (constant 2015 US$)'], errors='coerce')

# Filtra solo l'anno 2022
df_2022 = df[df['year'] == 2022]

# Elimina i valori mancanti di GDP per capita
df_2022 = df_2022.dropna(subset=['GDP per capita (constant 2015 US$)'])

# Trova i 10 paesi con GDP per capita più alto
top10_highest_countries = df_2022.sort_values(by='GDP per capita (constant 2015 US$)', ascending=False).head(10)['country'].tolist()

# Trova i 10 paesi con GDP per capita più basso
top10_lowest_countries = df_2022.sort_values(by='GDP per capita (constant 2015 US$)', ascending=True).head(10)['country'].tolist()

# Stampa solo le liste dei paesi
print("TOP 10 paesi con GDP per capita più ALTO nel 2022:")
print(top10_highest_countries)

print("\nTOP 10 paesi con GDP per capita più BASSO nel 2022:")
print(top10_lowest_countries)
TOP 10 paesi con GDP per capita più ALTO nel 2022:
['Monaco', 'Bermuda', 'Luxembourg', 'Ireland', 'Switzerland', 'Cayman Islands', 'Norway', 'Singapore', 'United States', 'Qatar']

TOP 10 paesi con GDP per capita più BASSO nel 2022:
['Burundi', 'Afghanistan', 'Central African Republic', 'Madagascar', 'Somalia', 'Congo, the Democratic Republic of the', 'Malawi', 'Niger', 'Chad', 'Mozambique']
Code 
# Libraries
import pandas as pd
import plotly.express as px

# Load the dataset
df = pd.read_csv("unicef_indicator_1 (2).csv")

# Lista dei 10 paesi che ti interessano
countries_of_interest = [
    'Burundi', 'Afghanistan', 'Central African Republic', 'Madagascar', 
    'Somalia', 'Congo, the Democratic Republic of the', 'Malawi', 
    'Niger', 'Chad', 'Mozambique'
]

# Filtro il dataset: solo maschi e femmine, e solo i 10 paesi
df_filtered = df[
    (df["sex"].isin(["Female", "Male"])) &
    (df["country"].isin(countries_of_interest))
]

# Calcolo la media per anno e sesso
df_grouped = df_filtered.groupby(["time_period", "sex"], as_index=False)["obs_value"].mean()

# Creo il grafico a linee
fig = px.line(
    df_grouped,
    x="time_period",
    y="obs_value",
    color="sex",
    title="<b>Healthcare Access for Children with Diarrhea (Top 10 Poorest Countries)</b>",
    labels={
        "time_period": "Year",
        "obs_value": "Average Value (%)",
        "sex": "Sex"
    },
    color_discrete_map={
        "Female": "#FF69B4",  # Rosa
        "Male": "#00BFFF"     # Azzurro
    }
)

# Miglioro l'aspetto
fig.update_traces(mode="lines+markers")  # Linee con pallini
fig.update_layout(
    template="plotly_white",
    xaxis=dict(tickmode="linear"),  # Mostra tutti gli anni
    yaxis=dict(range=[0, 100])      # Range percentuale 0-100
)

fig